What is MCP Jenkins Server and what does it do?
It is an MCP server that exposes Jenkins functions as tools, allowing MCP clients (such as AI assistants) to query Jenkins server info, list jobs, inspect builds, manage views, and trigger builds.
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Basic information
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No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
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MCP Jenkins Server is a Model Context Protocol server that integrates with Jenkins, providing tools to interact with Jenkins jobs, builds, views, and server information. It is intended for developers using MCP-compatible clients who need programmatic access to Jenkins automation infrastructure.
Configuration requires a .env file with JENKINS_URL, JENKINS_USER, and JENKINS_TOKEN (or alternate env variables JENKINS_USERNAME and JENKINS_PASSWORD in CLI setup). The server can be launched via uv with the provided JSON MCP configuration. Python 3.10+ and a Jenkins server with API access are required. Example client code: from mcp.client import Client; client = Client("http://localhost:8000"); response = client.call_tool("list_jobs").
It is an MCP server that exposes Jenkins functions as tools, allowing MCP clients (such as AI assistants) to query Jenkins server info, list jobs, inspect builds, manage views, and trigger builds.
Create a `.env` file with `JENKINS_URL`, `JENKINS_USER`, and `JENKINS_TOKEN`. Alternatively, set environment variables `JENKINS_URL`, `JENKINS_USERNAME`, and `JENKINS_PASSWORD` in the MCP client configuration.
Python 3.10 or later, a Jenkins server with API access enabled, and a valid Jenkins API token (or username/password) for authentication.
The server provides seven tools: `get_jenkins_info`, `list_jobs`, `get_job_info` (parameter: `job_name`), `get_build_info` (parameters: `job_name`, `build_number`), `get_build_console_output` (parameters: `job_name`, `build_number`), `get_views`, and `trriger_llm_demo_job_build` (parameter: `user`).
The example client uses an HTTP endpoint (`http://localhost:8000`). The CLI configuration launches the server using `uv` with the `server.py` script, which uses stdio MCP transport. Both approaches are described in the README.

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